A Data-Driven Model of an Appointment-Generated Arrival Process at an Outpatient Clinic
通过分析内分泌科门诊的预约和到达数据，发现每日预约安排本身是到达过程的主要变异来源，并构建了高保真仿真模型。
We develop a high-fidelity simulation model of the patient arrival process to an endocrinology clinic by carefully examining appointment and arrival data from that clinic. The data include the time that the appointment was originally made as well as the time that the patient actually arrived, as well as if the patient did not arrive at all, in addition to the scheduled appointment time. We take a data-based approach, specifying the schedule for each day by its value at the end of the previous day. This data-based approach shows that the schedule for a given day evolves randomly over time. Indeed, in addition to three recognized sources of variability—(i) no-shows, (ii) extra unscheduled arrivals, and (iii) deviations in the actual arrival times from the scheduled times—we find that the primary source of variability in the arrival process is variability in the daily schedule itself. Even though service systems with arrivals by appointment can differ in many ways, we think that our data-based approach to modeling the clinic arrival process can be a guideline or template for constructing high-fidelity simulation models for other arrival processes generated by appointments. The online supplement is available at https://doi.org/10.1287/ijoc.2017.0773 .